To synthesize high-quality novel views, the coarse depth map is usually estimated first followed by a Depth-Image-Based Rendering (DIBR) module [1]}, [2]}. Based on our empirical observations (see the Appendix for details), such a procedure also works well for 360 novel view synthesis with different configurations of coarse depth estimation networks. Considering both performance and computation cost, in this paper we suggest using a lightweight network: CoordNet [3]} for doing the task. CoordNet utilizes coordinate convolution [4]} to enforce 360 awareness. We
append an atrous spatial pyramid pooling module (ASPP) [5]} to the end of the encoder to better aggregate multi-scale context information. Note that PanoDepth is model-agnostic, thus any depth estimation network can be used here to fulfill specific requirement.
The estimated coarse depth map and the original ERP image are then used to render multiple synthesized views of predefined baselines via a differentiable DIBR operation [6]}. In this paper, we choose to use vertical baselines instead of horizontal ones. The analysis of this choice can be found in the Appendix.
